scholarly journals Hard-threshold neural network-based prediction of organic synthetic outcomes

2020 ◽  
Author(s):  
Haoyang Hu ◽  
Zhihong Yuan
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Organic Molecules ◽  
Backpropagation Algorithm ◽  
Forward Reaction ◽  
Drug Discovery And Development ◽  
Reaction Prediction ◽  
The Neural Network ◽  
Reaction Data ◽  
Retrosynthetic Analysis

Abstract Retrosynthetic analysis is a canonical technique for planning the synthesis route of organic molecules in drug discovery and development. In this technique, the screening of synthetic tree branches requires accurate forward reaction prediction, but existing software is far from completing this step independently. Previous studies attempted to apply a neural network to forward reaction prediction, but the accuracy was not satisfying. Through using the Edit Vector-based description and extended-connectivity fingerprints to transform the reaction into a vector, this study focuses on the update of the neural network to improve the template-based forward reaction prediction. Hard-threshold activation and the target propagation algorithm are implemented by introducing mixed convex-combinatorial optimization. Comparative tests were conducted to explore the optimal hyperparameter set. Using 15,000 experimental reaction data extracted from granted United States patents, the proposed hard-threshold neural network was systematically trained and tested. The results demonstrated that a higher prediction accuracy was obtained than that for the traditional neural network with backpropagation algorithm. Some successfully predicted reaction examples are also briefly illustrated.

scholarly journals Hard-threshold-Neural-Network based Prediction of Organic Synthetic Outcomes

2020 ◽  
Author(s):  
Haoyang Hu ◽  
Zhihong Yuan
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Organic Molecules ◽  
Backpropagation Algorithm ◽  
Forward Reaction ◽  
Drug Discovery And Development ◽  
Reaction Prediction ◽  
The Neural Network ◽  
Reaction Data ◽  
Retrosynthetic Analysis

Abstract Retrosynthetic analysis is a canonical technique for planning the synthesis route of organic molecules in drug discovery and development. In this technique, the screening of synthetic tree branches requires accurate forward reaction prediction, but existing software is far from completing this step independently. Previous studies attempted to apply a neural network to forward reaction prediction, but the accuracy was not satisfying. Through using the Edit Vector-based description and extended-connectivity fingerprints to transform the reaction into a vector, this study focuses on the update of the neural network to improve the template-based forward reaction prediction. Hard-threshold activation and the target propagation algorithm are implemented by introducing mixed convex-combinatorial optimization. Comparative tests were conducted to explore the optimal hyperparameter set. Using 15,000 experimental reaction data extracted from granted United States patents, the proposed hard-threshold neural network was systematically trained and tested. The results demonstrated that a higher prediction accuracy was obtained than that for the traditional neural network with backpropagation algorithm. Some successfully predicted reaction examples are also briefly illustrated.

scholarly journals Hard-threshold-Neural-Network based Prediction of Organic Synthetic Outcomes

2020 ◽  
Author(s):  
Haoyang Hu ◽  
Zhihong Yuan
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Organic Molecules ◽  
Backpropagation Algorithm ◽  
Forward Reaction ◽  
Comparative Tests ◽  
Drug Discovery And Development ◽  
Reaction Prediction ◽  
Propagation Algorithm ◽  
Retrosynthetic Analysis

Abstract Retrosynthetic analysis is the canonical technique to plan the synthesis route of organic molecules in drug discovery and development. In this technique, the screening of synthetic tree branches requires accurate forward reaction prediction, but existing software is still far from completing this step independently. Previous studies have attempted to apply neural network in the forward reaction prediction, but the accuracy is not satisfying. Through using the Edit Vector based description and Extended-Connectivity Fingerprints to transform reaction into vector, the presented work focuses on the update of neural network to improve the template-based forward reaction prediction. Hard-threshold activation and target propagation algorithm are implemented by introducing the mixed-convex combinatorial optimization. Comparative tests are conducted to explore the optimal hyperparameter set. Using 15, 000 experimental reaction extracted from granted United States patents, the proposed hard-threshold neural network is systematically trained and tested. The results demonstrate that a higher prediction accuracy is obtained when compared to the traditional neural network with backpropagation algorithm. Some successfully predicted reaction examples are also briefly illustrated.

scholarly journals Hard-threshold-Neural-Network based Prediction of Organic Synthetic Outcomes

2019 ◽  
Author(s):  
Haoyang Hu ◽  
Zhihong Yuan
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Organic Molecules ◽  
Backpropagation Algorithm ◽  
Forward Reaction ◽  
Comparative Tests ◽  
Reaction Prediction ◽  
Medicine Development ◽  
Propagation Algorithm ◽  
Retrosynthetic Analysis

Abstract Retrosynthetic analysis is the canonical technique to plan the synthesis route of organic molecules in medicine development. In this technique, the screening of synthetic tree branches requires accurate forward reaction prediction, but existing software is still far from completing this step independently. Previous studies have attempted to apply neural network in the forward reaction prediction, but the accuracy is not satisfying. Through using the Edit-based Description and Extended-Connectivity Fingerprints to transform reaction into vector, the presented work focuses on the update of neural network to improve the template-based forward reaction prediction. Hard-threshold activation and target propagation algorithm are implemented by introducing the mixed-convex combinatorial optimization. Comparative tests are conducted to explore the optimal hyperparameter set. Using 15 000 experimental reaction records from granted United States patents, the proposed hard-threshold neural network is systematically trained and tested. The results demonstrate that a higher prediction accuracy is obtained when compared to the traditional neural network with backpropagation algorithm. Indeed, the prediction accuracy of the proposed hard-threshold neural network can reach 73.9% which is higher than Coley’s result with 71.8% ( Coley et al. ACS Cent. Sci, 2017 ). Some successfully predicted reaction examples are also briefly discussed.

scholarly journals MODEL JARINGAN SARAF TIRUAN UNTUK EVALUASI RESIKO KREDIT

2018 ◽  
Vol 2 (1) ◽  
pp. 76
Author(s):  
Novan Wijaya
Keyword(s):  
Neural Network ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Financial Risk ◽  
Banking Sector ◽  
Major Focus ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Risk Evaluation System

Credit risk evaluation is an importanttopic in financial risk management and become a major focus in the banking sector. This research discusses a credit risk evaluation system using an artificial neural network model based on backpropagation algorithm. This system is to train and test the neural network to determine the predictive value of credit risk, whether high riskorlow risk. This neural network uses 14 input layers, nine hidden layers and an output layer, and the data used comes from the bank that has branches in EastJakarta. The results showed that neural network can be used effectively in the evaluation of credit risk with accuracy of 88% from 100 test data

scholarly journals Pattern Recognition and Neural Network-Driven Roller Track Analysis via 5G Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yuliang Guo
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Data Storage ◽  
Prediction Accuracy ◽  
Video Monitor ◽  
Advanced Technologies ◽  
The Neural Network ◽  
5G Network ◽  
Processing Module ◽  
Track Analysis

Roller skating is an important and international physical exercise, which has beautiful body movements to be watched. However, the falling of roller athletes also happens frequently. Upon the roller athletes’ fall, it means that the whole competition is over and even the roller athletes are perhaps injured. In order to stave off the tragedy, the roller track can be analyzed and be notified the roller athlete to terminate the competition. With such consideration, this paper analyzes the roller track by using two advanced technologies, i.e., pattern recognition and neural network, in which each roller athlete is equipped with an automatic movement identifier (AMI). Meanwhile, AMI is connected with the remote video monitor referee via the transmission of 5G network. In terms of AMI, its function is realized by pattern recognition, including data collection module, data processing module, and data storage module. Among them, the data storage module considers the data classification based on roller track. In addition, the neural network is used to train the roller tracks stored at AMI and give the further analysis results for the remote video monitor referee. Based on NS3, the devised AMI is simulated and the experimental results reveal that the prediction accuracy can reach 100% and the analyzed results can be used for the falling prevention timely.

scholarly journals Modelling of Deformation Resistance with Big Data and Its Application in the Prediction of Rolling Force of Thick Plate

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shun Hu Zhang ◽  
Li Zhi Che ◽  
Xin Ying Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Rolling Force ◽  
Deformation Resistance ◽  
Force Model ◽  
The Neural Network ◽  
Bp Neural Network Model

The precision of traditional deformation resistance model is limited, which leads to the inaccuracy of the existing rolling force model. In this paper, the back propagation (BP) neural network model was established according to the industrial big data to accurately predict the deformation resistance. Then, a new rolling force model was established by using the BP neural network model. During the establishment of the neural network model, the data set of deformation resistance was established, which was calculated back from the actual rolling force data. Based on the data set after normalization, the BP neural network model of deformation resistance was established through the optimization of algorithm and network structure. It is shown that both the prediction accuracy of the neural network model on the training set and the test set are high, indicating that the generalization ability of the model is strong. The neural network model of the deformation resistance is compared with the theoretical one, and the maximum error is only 3.96%. Furthermore, by comparison with the traditional rolling force model, it is found that the prediction accuracy of the rolling force model imbedding with the present neural network model is improved obviously. The maximum error of the present rolling force model is just 3.86%. The research in this paper provides a new way to improve the prediction accuracy of rolling force model.

scholarly journals Data Mining using Artificial Neural Network POS_NEG Rules

2013 ◽  
Vol 11 (6) ◽  
pp. 2709-2714
Author(s):  
Pushkar Shinde ◽  
Dr. Varsha Patil
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Sensitivity Method

Diabetes patients are increasing in number so it is necessary to predict , treat and diagnose the disease. Data Mining can help to provide knowledge about this disease. The knowledge extracted using Data Mining can help in treating and preventing the disease. Artificial Neural Network (ANN) can be used to create an classifier from the data. The neural network is trained using backpropagation algorithm The knowledge stored in the neural network is used to predict the disease. The knowledge stored in neural network is extracted using Pos-Neg sensitivity method. The knowledge extracted is in form of sensitivity analysis to analyze the disease and in turn help in treating the disease.

Study on Displacement Prediction Model of Foundation Pit

2013 ◽  
Vol 834-836 ◽  
pp. 679-682
Author(s):  
Qiang Song ◽  
Jun Jian Zhang ◽  
Yun Sheng Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Prediction Model ◽  
Prediction Error ◽  
Prediction Accuracy ◽  
Foundation Pit ◽  
Displacement Prediction ◽  
Fitness Evaluation ◽  
The Neural Network

The prediction model is proposed in this paper to predict the displacement of foundation pit. In the model, genetic algorithms is applied to optimize the node function of the neural network (15 node function coefficients are optimized simultaneously). Next, do the further optimization to the model, and GA-transFcn3 Model is established whose fitness evaluation takes into account the multi-step prediction error. Finally, it is verified that the GA-transFcn3 Model created in this article has the desirable prediction accuracy through engineering examples. The establishment of GA-transFcn3 Model can provide researchers and engineers with ideas and methods for the displacement prediction of foundation pit, and can be popularized and applied in practical projects.

scholarly journals Completion of Partial Reaction Equations

2020 ◽  
Author(s):  
Alain C. Vaucher ◽  
Philippe Schwaller ◽  
Teodoro Laino
Keyword(s):  
Deep Learning ◽  
Chemical Transformations ◽  
Learning Models ◽  
Forward Reaction ◽  
Partial Reaction ◽  
Reaction Prediction ◽  
Retrosynthetic Analysis ◽  
Incomplete Reaction ◽  
Reaction Equations ◽  
Deep Learning Model

We present a deep-learning model for inferring missing molecules in reaction equations. Such an algorithm features multiple interesting behaviors. First, it can infer the necessary reagents and solvents in chemical transformations specified only in terms of main compounds, as often resulting from retrosynthetic analyses. The completion with necessary reagents ensures that reaction equations are compatible with deep-learning models relying on a complete reaction specification. Second, it can cure existing datasets by detecting missing compounds, such as reagents that are essential for given classes of reactions. Finally, this model is a generalization of models for forward reaction prediction and retrosynthetic analysis, as both can be formulated in terms of incomplete reaction equations. We illustrate that a single trained model, based on the transformer architecture and acting on reaction SMILES strings, can address all three points.<br><br>Workshop paper at the Machine Learning for Molecules Workshop at NeurIPS 2020.<br>

scholarly journals Spatial Pattern Evaluation of Rural Tourism via the Multifactor-Weighted Neural Network Model in the Big Data Era

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jiangrui Zhu ◽  
Feng Jian
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Spatial Pattern ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Accuracy ◽  
Market Competition ◽  
Rural Tourism ◽  
Tourist Attractions ◽  
The Neural Network

The exploration of the evaluation effect of rural tourism spatial pattern based on the multifactor-weighted neural network model in the era of big data aims to optimize the spatial layout of rural tourist attractions. There are plenty of problems such as improper site selection, layout dispersion, and market competition disorder of rural tourism caused by insufficient consideration of planning and tourist market. Hence, the multifactor model after simple weighting is combined with the neural network to construct a spatiotemporal convolution neural network model based on multifactor weighting here to solve these problems. Moreover, the simulation experiment is conducted on the spatial pattern of rural tourism in the Ningxia Hui Autonomous Region to verify the evaluation performance of the constructed model. The results show that the prediction accuracy of the model is 97.69%, which is at least 2.13% higher than that of the deep learning algorithm used by other scholars. Through the evaluation and analysis of the spatial pattern of rural tourist attractions, the spatial distribution of scenic spots in Ningxia has strong stability from 2009 to 2019. Meanwhile, the number of scenic spots in the seven plates has increased and the time cost of scenic spot accessibility has changed significantly. Besides, the change rate of the one-hour isochronous cycle reaches 41.67%. This indicates that the neural network model has high prediction accuracy in evaluating the spatial pattern of rural tourist attractions, which can provide experimental reference for the digital development of the spatial pattern of rural tourism.

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